Masking sensitive information in a document

ABSTRACT

The exemplary embodiments disclose a method, a computer program product, and a computer system for protecting sensitive information. The exemplary embodiments may include using an inverted text index for evaluating one or more statistical measures of an index token of the inverted text index, using the one or more statistical measures for selecting a set of candidate tokens, extracting metadata from the inverted text index, associating the set of candidate tokens with respective token metadata, tokenizing at least one document resulting in one or more document tokens, comparing the one or more document tokens with the set of candidate tokens, selecting a set of document tokens to be masked, selecting at least part of the set of document tokens that comprises sensitive information according to the associated token metadata, masking the at least part of the set of document tokens, and providing one or more masked documents.

BACKGROUND

The present invention relates to the field of digital computer systems,and more specifically, to a method for masking sensitive information ina document.

Data protection and keeping sensitive information private is veryimportant for companies and their customers. However, technicalchallenges of data privacy protection are growing with a trend of movingservices to third parties and into the cloud with increased diversity ofdata.

SUMMARY

The exemplary embodiments disclose a method, a computer program product,and a computer system for protecting sensitive information. Theexemplary embodiments may include using an inverted text index forevaluating one or more statistical measures of an index token of theinverted text index, using the one or more statistical measures forselecting a set of candidate tokens, extracting metadata from theinverted text index, associating the set of candidate tokens withrespective token metadata, tokenizing at least one document resulting inone or more document tokens, comparing the one or more document tokenswith the set of candidate tokens, selecting a set of document tokens tobe masked, selecting at least part of the set of document tokens thatcomprises sensitive information according to the associated tokenmetadata, masking the at least part of the set of document tokens, andproviding one or more masked documents.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The following detailed description, given by way of example and notintended to limit the exemplary embodiments solely thereto, will best beappreciated in conjunction with the accompanying drawings, in which:

FIG. 1 depicts an exemplary block diagram of a system, in accordancewith the exemplary embodiments.

FIG. 2 depicts an exemplary flowchart illustrating a method forprotecting sensitive information in documents, in accordance with theexemplary embodiments.

FIG. 3 depicts an exemplary flowchart illustrating a method forproviding a set of candidate tokens which may comprise sensitiveinformation, in accordance with the exemplary embodiments.

FIG. 4 depicts an exemplary flowchart illustrating a method forprotecting sensitive information in documents, in accordance with theexemplary embodiments.

FIG. 5 depicts an exemplary flowchart illustrating a method forprotecting sensitive information in documents, in accordance with theexemplary embodiments.

FIG. 6 depicts an exemplary block diagram depicting the hardwarecomponents of the system, in accordance with the exemplary embodiments.

FIG. 7 depicts a cloud computing environment, in accordance with theexemplary embodiments.

FIG. 8 depicts abstraction model layers, in accordance with theexemplary embodiments.

The drawings are not necessarily to scale. The drawings are merelyschematic representations, not intended to portray specific parametersof the exemplary embodiments. The drawings are intended to depict onlytypical exemplary embodiments. In the drawings, like numberingrepresents like elements.

DETAILED DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. The exemplary embodiments are onlyillustrative and may, however, be embodied in many different forms andshould not be construed as limited to the exemplary embodiments setforth herein. Rather, these exemplary embodiments are provided so thatthis disclosure will be thorough and complete, and will fully convey thescope to be covered by the exemplary embodiments to those skilled in theart. In the description, details of well-known features and techniquesmay be omitted to avoid unnecessarily obscuring the presentedembodiments.

References in the specification to “one embodiment,” “an embodiment,”“an exemplary embodiment,” etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to implement such feature, structure, orcharacteristic in connection with other embodiments whether or notexplicitly described.

In the interest of not obscuring the presentation of the exemplaryembodiments, in the following detailed description, some processingsteps or operations that are known in the art may have been combinedtogether for presentation and for illustration purposes and in someinstances may have not been described in detail. In other instances,some processing steps or operations that are known in the art may not bedescribed at all. It should be understood that the following descriptionis focused on the distinctive features or elements according to thevarious exemplary embodiments.

The descriptions of the various embodiments of the present inventionwill be presented for purposes of illustration but are not intended tobe exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

The requested document may be a structured or unstructured document. Bycontrast to a structured document, the unstructured document maycomprise unstructured information that either does not have a predefineddata model or is not organized in a predefined manner. This may makedifficult an understanding of such documents using programs as comparedto data stored in fielded form in databases or annotated in documents ofstructured documents. The document may, for example, be an electronicdocument. The electronic document may be an electronic media contentthat is intended to be used in either an electronic form or as printedoutput. The electronic document may comprise, for example, a web page, adocument embedded in a web page and that may be rendered in the webpage, spreadsheet, email, book, picture, and presentation that have anassociated user agent such as a document reader, editor or media player.

A typical application scenario for documents may include loading adocument and displaying it on interfaces to different users. However,this may result in data which includes sensitive information beingcopied to a less-trusted environment. The sensitive information maycomprise private data such as social-security numbers, passport data,credit card numbers, health-record details, etc. which should not beleaked to untrusted parties. The present subject matter may solve thisissue by using data masking. The purpose of data masking may be tode-sensitize the data, so as to hide or mask sensitive data items, suchthat the data as a whole remains useful for its intended purpose. Thedata masking may be performed so that the access to the document fulfilspredefined data access rules. The data access rules may comprisegovernance policies and/or user access rights. For example, thegovernance rules may require protecting any kind of sensitiveinformation such as personal information.

However, conventional data masking may be very resource consuming as itmay require large infrastructure processing, settings and configurationsto work. The present subject matter may solve this issue by providingefficient and optimized data masking of documents. The present subjectmatter may be efficient in that it may use the same set of candidatetokens potentially containing sensitive information to identify thecontent to be masked in multiple requested documents. This may saveprocessing resources that would otherwise be required to process everydocument individually. The present subject matter may be optimal in thatthe inverted text index (also referred to as inverted index) may beprovided in different format e.g. compressed format, that enables aresource saving processing. The inverted text index may be an index datastructure storing a mapping from tokens, such as words or numbers, toits locations in a document or multiple documents of the set ofdocuments. For example, the inverted text index may be a hashmap datastructure. The tokens of the inverted text index may be referred to asthe index tokens.

According to one embodiment, the method further comprises: determiningtopic metadata of the set of candidate tokens. The topic metadata ofeach token of the set of candidate tokens indicates a topic of the tokenor a topic of a document containing the token, wherein the tokenmetadata of the token further comprises the topic metadata.

The topic metadata of a token may comprise an attribute valuerepresenting the topic of the token and one or more values of attributesdescriptive of the topic of the token. For example, if the token ismorbus-addison which is an orphan disease, the topic for that token maybe disease and some topic metadata may be indicators like orphandisease. It may also be possible that a token is associated withmultiple topics. Following the above example, the topics of the tokenmorbus-addison may be disease topic and the orphan disease topic. Thetopic metadata of the token may in addition or alternatively comprise agovernance rule that indicates values of the tokens of this topic thatneed to be confidential. For example, the topic of a given token mayrelate to a healthcare system and the governance rule may indicate thatinformation about patients should be treated confidentially. The contentof the token metadata of each candidate token may thus be processed todetermine if it contains sensitive information e.g. using datasensitive-non sensitive classifier. The classifier may, for example, usethe domain of the token to associate it with the governance catalog andthe rules defined for the domain may be used to classify the token. Thisprocessing may, for example, further comprise data mining to interpretand apply rules on attribute values present in the token metadata. Thedata mining may enable to check again the metadata with a businessglossary to see what is “known” about metadata and if any kind ofsensitivity classification is associated with it.

This embodiment may enable an accurate selection of the at least part ofthe set of candidate tokens as the selection may be based on a richcontent of the metadata.

According to one embodiment, the method further comprises inputting thetoken topic to an information governance tool and receiving as outputthe topic metadata. The information governance tool may, for example, bea company governance catalog solution.

According to one embodiment, the statistical measure of the index tokencomprises one or a combination of: number of documents of the set ofdocuments containing the index token, the frequency of occurrence of theindex token in the set of documents, the frequency of occurrence of atoken type of the index token in the set of documents, wherein selectingthe set of candidate tokens comprises comparing the statistical measurewith a predefined threshold. The token type may be a text type, numbertype, or a combination thereof. For example, a configurable thresholdmay be provided for each token type indicating how often a token isallowed to occur in the set of documents (posting list of tokens). Forexample, in case the frequency of occurrence of an index token issmaller than the configurable threshold, it may be selected as acandidate token that may potentially contain sensitive information. Thatis, the less frequent an index token is the more likely it is that thisis individual information that is suspicious and potentially have to bemasked. In another example, more than one statistical measure may beused to decide whether an index token is a candidate token thatpotentially contains sensitive information. Following the above example,if the frequency of occurrence of an index token is less than theconfigurable threshold, it may further be determined whether the numberof documents of the set of documents containing said index token issmaller than another configurable threshold, and if yes, the index tokenmay be selected as a candidate token.

According to one embodiment, the inverted text index is updated in casethe set of documents changes. The method further comprises: storing theset of candidate tokens in association with the token metadata in astorage system or data store, and repeatedly: performing the selectionof set of candidate tokens and the extraction of the metadata, andupdating the storage system accordingly, wherein the updated storagesystem is used for selecting the document tokens that are masked.

The present method may comprise a first method for providing the set ofcandidate tokens followed by a second method for masking a requesteddocument. The first method comprises the steps of the present methodexecuted before receiving the request of the document. And the secondmethod comprises the step of receiving the request of the document andfollowing steps of the present method. The second method may beperformed independently of the first method. For example, upon receivinga request of a document, the storage system may be read to retrieve theset of candidate tokens. The storage system may be updated using thefirst method with new set of candidate tokens independently of thesecond method. Thus, this embodiment may further improve the accuracy ofthe data masking as it may be based on up to date data.

According to one embodiment, selecting the at least part of the set ofdocument tokens that comprises sensitive information according to theassociated token metadata comprises running a classifier on the tokenmetadata, and classifying the set of document tokens as sensitive or notsensitive tokens, wherein the selection is performed based on theclassification. For example, if the token metadata comprise the tokentype, it may be used to decide whether a document token is to be maskedor not. If the document token is a combination of numbers and strings,this may indicate that it represents sensitive information. That is,only candidate tokens of a specific category or type may be masked. Thefrequency of occurrence of a token may be defined (first definition) asthe number of occurrences of the token divided by the number ofdocuments of the set of documents. In another example, the frequency ofoccurrence may be defined (second definition) as the number of documentscontaining the token divided by the number of documents in the set ofdocuments. If, for example, the token metadata further comprises thedocument IDs of the documents in which the index token is present, thedocument IDs may advantageously be used to mask or not mask the documenttoken if the frequency of occurrence, according to the first definition,of the document tokens is low i.e. smaller than a threshold. Forexample, since the frequency is defined with respect to all documents,the frequency may still be small even if the same token is present inonly one document because the frequency is computed using all documents.That is, if the same document token is present in only one document, tentimes it may indicate that it is not sensitive although the frequencymay be small. Thus, using the additional document IDs may enable todetect such cases e.g. as being non sensitive. If, for example, thetoken metadata further comprises a governance rule indicating that theusers located in a certain region are not allowed to access informationsuch as addresses of persons, then the classifier may determine thelocation of the user who requested the document and may decide whetherto mask the address information (which is part of the set of documenttokens) based on that location and in accordance with the rule.

According to one embodiment, the method further comprises: determining adomain represented by a content of the requested document, wherein theset of documents represent the determined domain and exclude therequested document. In other words, the requested document may have notbeen used to generate the inverted index; however, the inverted indexmay advantageously be used according to this embodiment as it requiresthat the requested document and the set of documents of the invertedindex have the same domain. This may enable, for example, to separatetest data used to provide the candidate tokens from data used toclassify tokens to be masked.

According to one embodiment, the set of documents comprise the requesteddocument. This may be advantageous as it may provide accurate selectionof set of document tokens and thus accurate data masking because thedocument tokens may be part of the index tokens.

FIG. 1 is a block diagram illustrating a document provider system 100 inaccordance with an example of the present subject matter. The documentprovider system 100 comprises a document retrieval system 101 and a usercomputer system 102. The document retrieval system 101 and the usercomputer system 102 may be operable for communication via a network 103.The network 103 may, for example, be the Internet, a local area network,a wide area network and/or a wireless network.

The document retrieval system 101 has access to documents in a storage,represented here by a database 105, operatively coupled to the documentretrieval system 101. The database 105 contains documents to be sent tothe user computer system 102 in operation. These documents may be anytype of mainly unstructured text, such as newspaper articles, realestate records or paragraphs in a manual. The document retrieval system101 may enable document retrieval. The document retrieval may be definedas a matching of some stated user query of the user computer system 102against one or more documents in the database 105. The documents 108stored in the database 105 may represent a set of one or more domains.For example, the documents 108 may comprise multiple sets of documentsrepresenting distinct domains respectively. A domain represents conceptsor categories which belong to a part of the world, such as biology orpolitics. The domain typically models domain-specific definitions ofterms. For example, a domain can refer to healthcare, advertising,commerce, medical and/or biomedical-specific field. The database 105 mayfurther comprise an inverted text index 109. The inverted text index maybe an index data structure storing a mapping from index tokens, such aswords or numbers, to its locations in a document or multiple documentsof the documents 108. For simplification of the description, only oneinverted text index is shown but it is not limited to e.g. the database105 may store one inverted index for each set of the multiple sets ofdocuments.

The document retrieval system 101 may be configured to protect sensitiveinformation in accordance with the present subject matter. For example,the document retrieval system 101 may implement a dynamic data maskingprocess whereby documents may be accessed, masked and sent to the usercomputer system 102 as required. In particular, the document to be sentto the user computer system 102 may contain certain tokens which may bemasked before the document is sent out. For example, the tokens to bemasked may dynamically be determined based on the context in which thedocument is requested or is to be sent to the user computer system 102.For example, depending on the data access rules, the same document mightbe masked differently depending on the locations of the user computersystem 102 and the user submitting the query to access the document. Forexample, European (EU) or United States (US) data may be maskedaccording to General Data Protection Regulation (GDPR) or Federal Law.

The user computer system 102 may receive the masked document via thenetwork 103 and stores the masked document in storage, represented hereby database 106, operatively coupled to the user computer system 102.

FIG. 2 is a flowchart of a method for protecting sensitive informationin documents in accordance with an example of the present subjectmatter. For the purpose of explanation, the method described in FIG. 2may be implemented in the system illustrated in FIG. 1 but is notlimited to this implementation. The method of FIG. 2 may, for example,be performed by the document retrieval system 101.

The inverted text index 109 may be used in step 201 for evaluating oneor more statistical measures of index tokens of the inverted text index109. The statistical measure of the index token may comprise one or acombination of: number of documents of the set of documents 108containing the index token, the frequency of occurrence of the indextoken in the set of documents and the frequency of occurrence of a tokentype of the index token in the set of documents. The statisticalmeasures may be evaluated using information contained in the invertedtext index 109. The combination of the number of documents and thefrequency may, for example, be an array or vector or list etc. havingtwo elements comprising said number of documents and frequency.

The evaluated statistical measures may be used for selecting in step 203from the index tokens of the inverted text index 109 a set of candidatetokens that may contain sensitive information. For that, the statisticalmeasure of each index token of the inverted text index 109 may becompared with a threshold. The threshold may be a configurablethreshold. Based on the comparison result, each index token may beselected as candidate token. For example, if an index token is notfrequently present in the set of documents 108, that index token may beselected as a candidate token. For that, the frequency of occurrence ofthe index token in the set of documents 108 may be compared with amaximum frequency value. If the frequency of cooccurrence is less thanthe maximum frequency value, the index token may be selected as acandidate token. This may result in a set of N candidate tokensT_(cand1), T_(cand2) . . . T_(candN).

Metadata descriptive of the set of candidate tokens may be extracted instep 205 from the inverted text index 109. The extracted metadata of theindex token may comprise at least a token type of the index token and adocument identifier of a document containing the index token.

The set of candidate tokens may be associated in step 207 withrespective token metadata, wherein the token metadata of the tokencomprises the extracted metadata of the token. The set of candidatetokens may, for example, be stored in a data store, in association withthe token metadata e.g. the candidate tokens may be stored with somemetadata around token type, document frequency, and potential otherinformation like document metadata (e.g. type) that is present in theinverted text index, this also includes the document IDs of thedocuments this token was in.

A request of at least one document of the documents 108 may be receivedin step 209. The request may, for example, be a user query that mayrange from multi-sentence full descriptions of an information need to afew words. The request of the document may be received from the usercomputer system 102. The user computer system 102 may, for example,receive a data analysis request from a user of the user computer system102. The data analysis request may comprise natural language data. Anatural language processing of the data analysis request may beperformed by the user computer system 102 resulting in the request thatis sent by the user computer system 102. The term “user” refers to anentity e.g., an individual, another computer, or an applicationexecuting on the user computer system 102.

Upon receiving the request, the requested document may be tokenized instep 211 to obtain document tokens. This may result in a tokenizeddocument which may be a document represented as a collection of words(document tokens). This step 211 may be optional if, for example, theinverted text index comprises index tokens of the requested document. Inthis case, those index tokens may be provided as the document tokens ofthe requested document e.g. without having to tokenize anew therequested document.

The document tokens of the requested document may be compared in step213 with the set of candidate tokens. For that, the set of candidatetokens may be read from the data store. This may be advantageous as thedata store may comprise the up to date version of the set of candidatetokens.

Based on the comparison result, a set of document tokens to be maskedmay be selected in step 215 from all the document tokens of therequested document. The comparison result may, for example, indicatewhich document tokens are part of the set of candidate tokens. Thus, theselected set of document tokens may comprise document tokens that belongto the set of candidate tokens. In addition, the comparison result may,for example, indicate the document tokens which are semantically relatedto the set of candidate tokens. Those semantically related documenttokens may be part of the set of document tokens. For example, if thecandidate token indicates the age of a patient (that should beconfidential in certain contexts), a document token comprising the birthdate may be semantically related to that candidate token. Theinformation about semantic relations of a token may, for example, bepart of the token metadata. This step 215 may result in a set of Mdocument tokens T_(doc1), T_(doc2) . . . T_(docM). Each of the Mdocument tokens T_(doc1), T_(doc2) . . . T_(docM) may be associated withone or more candidate tokens T_(cand1), T_(cand2) . . . T_(candN). Forexample, two or more document tokens may be associated with differentcandidate tokens or with a same candidate token. Each document token ofthe set of M document tokens may be associated with the token metadataof the corresponding candidate token(s).

At least part of the set of document tokens that comprises sensitiveinformation according to the associated token metadata may be selectedin step 217. The at least part of the set of document tokens maycomprise L document tokens T_(doc1), T_(doc2) . . . T_(docL), where L≤M.The selection may be performed by processing the token metadataassociated with the set of document tokens. This step 217 may, forexample, be automatically performed. For example, if the token isassociated with a topic/term that can be also found or associated withinthe governance catalog with a business term, rules defined for thisbusiness term can be applied. E.g. if the business term is defined assensitive, the candidate token may be masked.

The at least part of the set of document tokens may be masked in step219 in the document, resulting in a masked document. For example, themasking may be performed via hashing. Here, a document token may behashed together with a long-term hash key. The document token may thenbe replaced with the resulting hash value in the masked document sent tothe user. Other known methods which are based on substitution,shuffling, deletion (“nulling”), obfuscation, or perturbation techniquesmay be used for performing the masking.

The masked document may be provided in step 221. For example, thedocument retrieval system 101 may send in response to the request themasked document to the user computer system 102.

Steps 211 to 221 may be repeated for each requested document in step209. For example, the requested documents may be processed concurrentlyor in parallel by the steps 211 to 221.

FIG. 3 is a flowchart of a method for providing a set of candidatetokens which may comprise sensitive information in accordance with anexample of the present subject matter. For the purpose of explanation,the method described in FIG. 3 may be implemented in the systemillustrated in FIG. 1 but is not limited to this implementation. Themethod of FIG. 3 may, for example, be performed by the documentretrieval system 101.

In step 300, an inverted text index 109 may be provided. The invertedtext index 109 is obtained from a set of documents e.g. the index tokensand associated information of the inverted text index are obtained fromthe set of documents.

The inverted text index 109 may be used in step 301 for evaluating oneor more statistical measures of index tokens of the inverted text index109. The statistical measure of the index token may comprise one or acombination of: number of documents of the set of documents 108containing the index token, the frequency of occurrence of the indextoken in the set of documents and the frequency of occurrence of a tokentype of the index token in the set of documents. The statisticalmeasures may be evaluated using information contained in the invertedtext index 109.

The evaluated statistical measures may be used for selecting in step 303from the index tokens of the inverted text index 109 a set of candidatetokens that may contain sensitive information. For that, the statisticalmeasure of each index token of the inverted text index 109 may becompared with a threshold. The threshold may be a configurablethreshold. Based on the comparison result, each index token may beselected as a candidate token. For example, if an index token is notfrequently present in the set of documents 108, that index token may beselected as candidate token. For that, the frequency of occurrence ofthe index token in the set of documents 108 may be compared with amaximum frequency value. If the frequency of cooccurrence is less thanthat the maximum frequency value, the index token may be selected as acandidate token.

Metadata descriptive of the set of candidate tokens may be extracted instep 305 from the inverted text index 109. The extracted metadata of theindex token may comprise at least a token type of the index token and adocument identifier of a document containing the index token.

The set of candidate tokens may be associated in step 307 withrespective token metadata, wherein the token metadata of the tokencomprises the extracted metadata of the token. The set of candidatetokens may be stored in a data store.

It may be determined (inquiry step 308) if the set of documents that arerepresented by the inverted index have changed. The change may, forexample, comprise the addition of one or more documents to the set ofdocuments, removing one or more documents of the set of documents and/orchanging content of one or more documents of the set of documents. Ifthe set of documents has changed, steps 300 to 308 may be repeated forgenerating a new inverted index and updating the data store with the newdetermined set of candidate tokens. If the set of documents has notchanged, the lastly provided or generated set of candidate tokens may bemaintained.

FIG. 4 is a flowchart of a method for protecting sensitive informationin documents in accordance with an example of the present subjectmatter. For the purpose of explanation, the method described in FIG. 4may be implemented in the system illustrated in FIG. 1 but is notlimited to this implementation. The method of FIG. 4 may, for example,be performed by the document retrieval system 101.

In step 400, a set of candidate tokens may be provided. The set ofcandidate tokes are tokens which may potentially represent sensitiveinformation. In one example, the set of candidate tokens may be providedusing the method of FIG. 3 e.g. the actual content of the data store maycomprise the set of candidate tokens provided in step 400. In anotherexample, the set of candidate tokens may be defined by one or more userse.g. the one or more users may be prompted with the set of documents 108and asked to provide the candidate tokens which may potentially comprisesensitive information and the set of candidate tokens may be receivedfrom the one or more users at the document retrieval system. Steps 409to 421 are steps 209 to 221 of FIG. 2 respectively. Steps 409 to 421 maybe performed by reading the content of the data store. Since the contentof the data store is provided by an independent method e.g. of FIG. 3,the provided set of candidate tokens of step 400 may change over time.

FIG. 5 is a flowchart of a method for protecting sensitive informationin documents in accordance with an example of the present subjectmatter. For the purpose of explanation, the method described in FIG. 5may be implemented in the system illustrated in FIG. 1 but is notlimited to this implementation. The method of FIG. 5 may, for example,be performed by the document retrieval system 101.

The method of FIG. 5 is similar to the method of FIG. 4, wherein steps409 to 421 are repeated for each further received request using theprovided set of candidate tokens in step 400. For example, the actualdata content of the data store as filled by the method of FIG. 3 may beused in each iteration.

FIG. 6 depicts a block diagram of devices within a system, in accordancewith the exemplary embodiments. It should be appreciated that FIG. 6provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironment may be made.

Devices used herein may include one or more processors 02, one or morecomputer-readable RAMs 04, one or more computer-readable ROMs 06, one ormore computer readable storage media 08, device drivers 12, read/writedrive or interface 14, network adapter or interface 16, allinterconnected over a communications fabric 18. Communications fabric 18may be implemented with any architecture designed for passing dataand/or control information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware components within a system.

One or more operating systems 10, and one or more application programs11 are stored on one or more of the computer readable storage media 08for execution by one or more of the processors 02 via one or more of therespective RAMs 04 (which typically include cache memory). In theillustrated embodiment, each of the computer readable storage media 08may be a magnetic disk storage device of an internal hard drive, CD-ROM,DVD, memory stick, magnetic tape, magnetic disk, optical disk, asemiconductor storage device such as RAM, ROM, EPROM, flash memory orany other computer-readable tangible storage device that can store acomputer program and digital information.

Devices used herein may also include a R/W drive or interface 14 to readfrom and write to one or more portable computer readable storage media26. Application programs 11 on said devices may be stored on one or moreof the portable computer readable storage media 26, read via therespective R/W drive or interface 14 and loaded into the respectivecomputer readable storage media 08.

Devices used herein may also include a network adapter or interface 16,such as a TCP/IP adapter card or wireless communication adapter (such asa 4G wireless communication adapter using OFDMA technology). Applicationprograms 11 on said computing devices may be downloaded to the computingdevice from an external computer or external storage device via anetwork (for example, the Internet, a local area network or other widearea network or wireless network) and network adapter or interface 16.From the network adapter or interface 16, the programs may be loadedonto computer readable storage media 08. The network may comprise copperwires, optical fibers, wireless transmission, routers, firewalls,switches, gateway computers and/or edge servers.

Devices used herein may also include a display screen 20, a keyboard orkeypad 22, and a computer mouse or touchpad 24. Device drivers 12interface to display screen 20 for imaging, to keyboard or keypad 22, tocomputer mouse or touchpad 24, and/or to display screen 20 for pressuresensing of alphanumeric character entry and user selections. The devicedrivers 12, R/W drive or interface 14 and network adapter or interface16 may comprise hardware and software (stored on computer readablestorage media 08 and/or ROM 06).

The programs described herein are identified based upon the applicationfor which they are implemented in a specific one of the exemplaryembodiments. However, it should be appreciated that any particularprogram nomenclature herein is used merely for convenience, and thus theexemplary embodiments should not be limited to use solely in anyspecific application identified and/or implied by such nomenclature.

Based on the foregoing, a computer system, method, and computer programproduct have been disclosed. However, numerous modifications andsubstitutions can be made without deviating from the scope of theexemplary embodiments. Therefore, the exemplary embodiments have beendisclosed by way of example and not limitation.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather, theexemplary embodiments are capable of being implemented in conjunctionwith any other type of computing environment now known or laterdeveloped.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or data center).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 7, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 40 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 40 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 7 are intended to be illustrative only and that computing nodes40 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 8, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 7) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 8 are intended to be illustrative only and the exemplaryembodiments are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and sensitive information masking 96.

The exemplary embodiments may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the exemplaryembodiments.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe exemplary embodiments may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the exemplary embodiments.

Aspects of the exemplary embodiments are described herein with referenceto flowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to the exemplaryembodiments. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousexemplary embodiments. In this regard, each block in the flowchart orblock diagrams may represent a module, segment, or portion ofinstructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the FIGS. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A computer implemented method for protectingsensitive information in documents, comprising: providing an invertedtext index for a set of documents; using the inverted text index forevaluating one or more statistical measures of an index token of theinverted text index; using the one or more statistical measures forselecting a set of candidate tokens that may contain sensitiveinformation; extracting metadata from the inverted text indexdescriptive of the set of candidate tokens, wherein the extractedmetadata comprises at least a token type of the index token and adocument identifier of a document containing the index token;associating the set of candidate tokens with respective token metadata,wherein the token metadata of the token comprises the extracted metadataof the token; receiving a request of at least one document; tokenizingthe at least one document, resulting in one or more document tokens;comparing the one or more document tokens with the set of candidatetokens; selecting a set of document tokens to be masked based on thecomparison; selecting at least part of the set of document tokens thatcomprises sensitive information according to the associated tokenmetadata; masking the at least part of the set of document tokens in theone or more documents, resulting in one or more masked documents; andproviding the one or more masked documents.
 2. The method of claim 1,further comprising: determining a topic metadata of the set of candidatetokens, the topic metadata of the set of candidate tokens comprising atopic of the set of candidate tokens or a topic of a document containingthe set of candidate tokens, wherein the token metadata of the tokenfurther comprises the topic metadata.
 3. The method of claim 2, furthercomprising: determining a token category of each token of the set ofcandidate tokens; inputting the token categories to an informationgovernance tool; and receiving as output the topic metadata.
 4. Themethod of claim 1, wherein: the statistical measure of the index tokencomprises one or more of a number of documents of the set of documentscontaining the index token, a frequency of occurrence of the index tokenin the set of documents, or a frequency of occurrence of a token type ofthe index token in the set of documents; and selecting the set ofcandidate tokens comprises comparing the statistical measure with apredefined threshold.
 5. The method of claim 4, wherein the token typecomprises one or more of a text type or number type.
 6. The method ofclaim 1, further comprising: storing the set of candidate tokens inassociation with the token metadata in a storage system; using anupdated inverted text index for evaluating one or more statisticalmeasures of an updated index token of the updated inverted text index;using the one or more statistical measures for selecting an updated setof candidate tokens that may contain sensitive information; extractingupdated metadata from the updated inverted text index descriptive of theupdated set of candidate tokens, wherein the extracted updated metadatacomprises at least a token type of the updated index token and adocument identifier of a document containing the updated index token;and updating the storage system accordingly, wherein the updated storagesystem is used for selecting updated document tokens that are masked. 7.The method of claim 1, wherein: selecting the at least part of the setof document tokens that comprises sensitive information according to theassociated token metadata comprises running a classifier on the tokenmetadata and classifying the set of document tokens as sensitive or notsensitive tokens; and the selection is performed based on theclassification.
 8. The method of claim 1, further comprising:determining a domain represented by a content of the requested document,wherein the set of documents represents the determined domain andexcludes the requested document.
 9. The method of claim 1, wherein theset of documents comprises the requested document.
 10. The method ofclaim 1, wherein the requested document is an unstructured document. 11.A computer program product for protecting sensitive information indocuments, the computer program product comprising: one or morenon-transitory computer-readable storage media and program instructionsstored on the one or more non-transitory computer-readable storage mediacapable of performing a method, the method comprising: providing aninverted text index for a set of documents; using the inverted textindex for evaluating one or more statistical measures of an index tokenof the inverted text index; using the one or more statistical measuresfor selecting a set of candidate tokens that may contain sensitiveinformation; extracting metadata from the inverted text indexdescriptive of the set of candidate tokens, wherein the extractedmetadata comprises at least a token type of the index token and adocument identifier of a document containing the index token;associating the set of candidate tokens with respective token metadata,wherein the token metadata of the token comprises the extracted metadataof the token; receiving a request of at least one document; tokenizingthe at least one document, resulting in one or more document tokens;comparing the one or more document tokens with the set of candidatetokens; selecting a set of document tokens to be masked based on thecomparison; selecting at least part of the set of document tokens thatcomprises sensitive information according to the associated tokenmetadata; masking the at least part of the set of document tokens in theone or more documents, resulting in one or more masked documents; andproviding the one or more masked documents.
 12. The computer programproduct of claim 11, further comprising: determining a topic metadata ofthe set of candidate tokens, the topic metadata of the set of candidatetokens comprising a topic of the set of candidate tokens or a topic of adocument containing the set of candidate tokens, wherein the tokenmetadata of the token further comprises the topic metadata.
 13. Thecomputer program product of claim 12, further comprising: determining atoken category of each token of the set of candidate tokens; inputtingthe token categories to an information governance tool; and receiving asoutput the topic metadata.
 14. The computer program product of claim 11,wherein: the statistical measure of the index token comprises one ormore of a number of documents of the set of documents containing theindex token, a frequency of occurrence of the index token in the set ofdocuments, or a frequency of occurrence of a token type of the indextoken in the set of documents; and selecting the set of candidate tokenscomprises comparing the statistical measure with a predefined threshold.15. The computer program product of claim 14, wherein the token typecomprises one or more of a text type or number type.
 16. A computersystem for protecting sensitive information in documents, the computersystem comprising: one or more computer processors, one or morecomputer-readable storage media, and program instructions stored on theone or more of the computer-readable storage media for execution by atleast one of the one or more processors capable of performing a method,the method comprising: providing an inverted text index for a set ofdocuments; using the inverted text index for evaluating one or morestatistical measures of an index token of the inverted text index; usingthe one or more statistical measures for selecting a set of candidatetokens that may contain sensitive information; extracting metadata fromthe inverted text index descriptive of the set of candidate tokens,wherein the extracted metadata comprises at least a token type of theindex token and a document identifier of a document containing the indextoken; associating the set of candidate tokens with respective tokenmetadata, wherein the token metadata of the token comprises theextracted metadata of the token; receiving a request of at least onedocument; tokenizing the at least one document, resulting in one or moredocument tokens; comparing the one or more document tokens with the setof candidate tokens; selecting a set of document tokens to be maskedbased on the comparison; selecting at least part of the set of documenttokens that comprises sensitive information according to the associatedtoken metadata; masking the at least part of the set of document tokensin the one or more documents, resulting in one or more masked documents;and providing the one or more masked documents.
 17. The computer systemof claim 16, further comprising: determining a topic metadata of the setof candidate tokens, the topic metadata of the set of candidate tokenscomprising a topic of the set of candidate tokens or a topic of adocument containing the set of candidate tokens, wherein the tokenmetadata of the token further comprises the topic metadata.
 18. Thecomputer system of claim 17, further comprising: determining a tokencategory of each token of the set of candidate tokens; inputting thetoken categories to an information governance tool; and receiving asoutput the topic metadata.
 19. The computer system of claim 16, wherein:the statistical measure of the index token comprises one or more of anumber of documents of the set of documents containing the index token,a frequency of occurrence of the index token in the set of documents, ora frequency of occurrence of a token type of the index token in the setof documents; and selecting the set of candidate tokens comprisescomparing the statistical measure with a predefined threshold.
 20. Thecomputer system of claim 19, wherein the token type comprises one ormore of a text type or number type.